class FitLoop(Loop): """This Loop iterates over the epochs to run the training Args: min_epochs: The minimum number of epochs max_epochs: The maximum number of epochs min_steps: The minimum number of steps max_steps: The maximum number of epoch .. note:: If neither the minimum epochs nor steps are specified the minimum number of epochs is set to 1 and if neither the maximum steps nor epochs are specified, the maximum epochs are set to 1000. """ def __init__( self, min_epochs: Optional[int] = None, max_epochs: Optional[int] = None, min_steps: Optional[int] = None, max_steps: Optional[int] = None ): super().__init__() self.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs self.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs self.epoch_loop = TrainingEpochLoop(min_steps, max_steps) @property def results(self) -> ResultCollection: return self.epoch_loop.results @property def current_epoch(self) -> int: """Return the current epoch""" return self.iteration_count @current_epoch.setter def current_epoch(self, value: int) -> None: """Setter for the current epoch""" self.iteration_count = value @property def global_step(self) -> int: """Returns the global step""" return self.epoch_loop.global_step @global_step.setter def global_step(self, value: int) -> None: """Sets the global step (forwards to epoch_loop)""" self.epoch_loop.global_step = value @property def total_batch_idx(self) -> int: """Returns the total number of batches already run (across all epochs)""" return self.epoch_loop.total_batch_idx @property def batch_idx(self) -> int: """Returns the number of batches already run within this epoch""" return self.epoch_loop.iteration_count @property def split_idx(self) -> int: """Returns the index of the current batch split (within the current batch) for bptt""" return self.epoch_loop.split_idx @property def min_steps(self) -> int: # TODO(@justusschock): Why aren't we using the attribute in this class? """Returns the minimum numnber of steps to run""" return self.epoch_loop.min_steps @property def max_steps(self) -> int: """Returns the maximum number of steps to run""" return self.epoch_loop.max_steps @max_steps.setter def max_steps(self, value: int) -> None: """Sets the maximum number of steps (forwards to epoch_loop)""" # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided self.epoch_loop.max_steps = value @property def running_loss(self) -> TensorRunningAccum: """Returns the running loss""" return self.epoch_loop.batch_loop.running_loss @property def _skip_backward(self) -> bool: """ Determines whether the loop will skip backward during automatic optimization. """ return self.epoch_loop.batch_loop._skip_backward @_skip_backward.setter def _skip_backward(self, value: bool) -> None: """ Determines whether the loop will skip backward during automatic optimization. """ self.epoch_loop.batch_loop._skip_backward = value @property def done(self) -> bool: """Evaluates when to leave the loop. Returns True if trainer.should_stop was set (e.g. by early stopping) or if the maximum number of steps or epochs is reached. """ # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop stop_steps = self.max_steps is not None and self.global_step >= self.max_steps stop_epochs = self.max_epochs is not None and self.current_epoch >= self.max_epochs should_stop = False if self.trainer.should_stop: # early stopping met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True met_min_steps = self.global_step >= self.min_steps if self.min_steps else True if met_min_epochs and met_min_steps: should_stop = True else: log.info( 'Trainer was signaled to stop but required minimum epochs' f' ({self.min_epochs}) or minimum steps ({self.min_steps}) has' ' not been met. Training will continue...' ) self.trainer.should_stop = should_stop return stop_steps or should_stop or stop_epochs @property def skip(self) -> bool: """Whether we should skip the training and immediately return from the call to :meth:`run`.""" return self.done or self.trainer.num_training_batches == 0 def connect(self, trainer: 'pl.Trainer', *args: Any, **kwargs: Any) -> None: """Connects the loop with necessary arguments like the trainer""" super().connect(trainer, *args, **kwargs) self.epoch_loop.connect(trainer) def reset(self) -> None: """Resets the internal state of this loop""" def on_run_start(self) -> None: """Calls the ``on_train_start`` hook.""" self.results.to(device=self.trainer.lightning_module.device) self.trainer.call_hook("on_train_start") def on_advance_start(self) -> None: """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and ``on_train_epoch_start``""" model = self.trainer.lightning_module # reset train dataloader if self.current_epoch != 0 and self.trainer.reload_dataloaders_every_epoch: self.trainer.reset_train_dataloader(model) # TODO: specify the possible exception with suppress(Exception): # set seed for distributed sampler (enables shuffling for each epoch) self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch) # changing gradient according accumulation_scheduler self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module) # stores accumulated grad fractions per batch self.epoch_loop.batch_loop.accumulated_loss = TensorRunningAccum( window_length=self.trainer.accumulate_grad_batches ) def advance(self) -> None: """Runs one whole epoch.""" train_dataloader = self.trainer.accelerator.process_dataloader(self.trainer.train_dataloader) train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader) with self.trainer.profiler.profile("run_training_epoch"): # run train epoch epoch_output = self.epoch_loop.run(train_dataloader) if epoch_output is None: return # the global step is manually decreased here due to backwards compatibility with existing loggers # as they expect that the same step is used when logging epoch end metrics even when the batch loop has # finished. this means the attribute does not exactly track the number of optimizer steps applied. # TODO(@carmocca): deprecate and rename so users don't get confused self.global_step -= 1 # log epoch metrics self.trainer.logger_connector.update_train_epoch_metrics() self.global_step += 1 def on_advance_end(self) -> None: """Updates the LR schedulers and does some internal bookkeeping""" if self.epoch_loop.batches_seen == 0: return self.epoch_loop.update_lr_schedulers('epoch', update_plateau_schedulers=True) did_train_only = self.trainer.disable_validation or self.trainer.evaluation_loop.skip if did_train_only: self.global_step -= 1 self._check_checkpoint_callback(True) self.global_step += 1 def on_run_end(self) -> None: """Runs teardown logic and calls the ``on_train_end`` hook""" # NOTE: the iteration_count/current_epoch is already incremented # Lightning today does not increment the current epoch at the last epoch run in Trainer.fit # To simulate that current behavior, we decrement here. # TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007 self.current_epoch -= 1 # trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates # when a checkpoint was saved at the last step self.epoch_loop.global_step -= 1 # TODO: see discussion/rework https://github.com/PyTorchLightning/pytorch-lightning/issues/7406 self._check_checkpoint_callback(should_update=True, is_last=True) self.epoch_loop.global_step += 1 # hook self.trainer.call_hook("on_train_end") # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers. # It might be related to xla tensors blocked when moving the cpu # kill loggers if self.trainer.logger is not None: self.trainer.logger.finalize("success") # summarize profile results self.trainer.profiler.describe() # give accelerators a chance to finish self.trainer.accelerator.on_train_end() # reset bookkeeping self.trainer._running_stage = None def should_accumulate(self) -> bool: """Whether the gradients should be accumulated""" return self.epoch_loop.batch_loop.should_accumulate() def _check_checkpoint_callback(self, should_update: bool, is_last: bool = False): """Checks if checkpointing needs to be done""" # TODO: bake this logic into the ModelCheckpoint callback if should_update and self.trainer.checkpoint_connector.has_trained: callbacks = self.trainer.checkpoint_callbacks if is_last and any(cb.save_last and cb.verbose for cb in callbacks): rank_zero_info("Saving latest checkpoint...") model = self.trainer.lightning_module for cb in callbacks: cb.on_validation_end(self.trainer, model)
class FitLoop(Loop[None]): """This Loop iterates over the epochs to run the training. Args: min_epochs: The minimum number of epochs max_epochs: The maximum number of epochs, can be set -1 to turn this limit off """ def __init__( self, min_epochs: int = 0, max_epochs: int = 1000, ) -> None: super().__init__() if max_epochs < -1: # Allow max_epochs to be zero, since this will be handled by fit_loop.done raise MisconfigurationException( f"`max_epochs` must be a non-negative integer or -1. You passed in {max_epochs}." ) self.max_epochs = max_epochs self.min_epochs = min_epochs self.epoch_loop = TrainingEpochLoop() self.epoch_progress = Progress() self._is_fresh_start_epoch: bool = True self._outputs: _EPOCH_OUTPUTS_TYPE = [] self._data_fetcher: Optional[AbstractDataFetcher] = None @property def total_batch_idx(self) -> int: """Returns the current batch index (across epochs)""" return self.epoch_loop.total_batch_idx @property def batch_idx(self) -> int: """Returns the current batch index (within this epoch)""" return self.epoch_loop.batch_idx @property def split_idx(self) -> int: """Returns the index of the current batch split (within the current batch) for bptt.""" return self.epoch_loop.batch_loop.split_idx @property def min_steps(self) -> Optional[int]: # TODO(@justusschock): Why aren't we using the attribute in this class? """Returns the minimum number of steps to run.""" return self.epoch_loop.min_steps @min_steps.setter def min_steps(self, value: Optional[int]) -> None: """Sets the minimum number of steps (forwards to epoch_loop)""" # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided self.epoch_loop.min_steps = value @property def max_steps(self) -> int: """Returns the maximum number of steps to run.""" return self.epoch_loop.max_steps @max_steps.setter def max_steps(self, value: int) -> None: """Sets the maximum number of steps (forwards to epoch_loop)""" # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided if value is None: rank_zero_deprecation( "Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7." " Use `max_steps = -1` instead.") value = -1 elif value < -1: raise MisconfigurationException( f"`max_steps` must be a non-negative integer or -1 (infinite steps). You passed in {value}." ) self.epoch_loop.max_steps = value @property def running_loss(self) -> TensorRunningAccum: """Returns the running loss.""" return self.epoch_loop.batch_loop.running_loss @Loop.restarting.setter def restarting(self, restarting: bool) -> None: # if the last epoch completely finished, we are not actually restarting, we can check this to see if all # current values are equal values = ( self.epoch_progress.current.ready, self.epoch_progress.current.started, self.epoch_progress.current.processed, ) finished_before_on_train_end = any( v != self.epoch_progress.current.completed for v in values) if finished_before_on_train_end: self.epoch_progress.current.completed = self.epoch_progress.current.processed restarting &= finished_before_on_train_end Loop.restarting.fset(self, restarting) # call the parent setter @property def prefetch_batches(self) -> int: is_unsized = self.trainer.num_training_batches == float("inf") inter_batch_parallelism = os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1" return 1 if is_unsized or inter_batch_parallelism else 0 @property def _skip_backward(self) -> bool: """Determines whether the loop will skip backward during automatic optimization.""" return self.epoch_loop.batch_loop.optimizer_loop._skip_backward @_skip_backward.setter def _skip_backward(self, value: bool) -> None: """Determines whether the loop will skip backward during automatic optimization.""" self.epoch_loop.batch_loop.optimizer_loop._skip_backward = value @property def _results(self) -> _ResultCollection: if self.trainer.training: return self.epoch_loop._results if self.trainer.validating: return self.epoch_loop.val_loop._results raise RuntimeError( "`FitLoop._results` property isn't defined. Accessed outside of scope" ) @property def done(self) -> bool: """Evaluates when to leave the loop.""" # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop stop_steps = _is_max_limit_reached(self.epoch_loop.global_step, self.max_steps) # `processed` is increased before `on_train_epoch_end`, the hook where checkpoints are typically saved. # we use it here because the checkpoint data won't have `completed` increased yet stop_epochs = _is_max_limit_reached( self.epoch_progress.current.processed, self.max_epochs) should_stop = False if self.trainer.should_stop: # early stopping met_min_epochs = self.epoch_progress.current.processed >= self.min_epochs if self.min_epochs else True met_min_steps = self.epoch_loop.global_step >= self.min_steps if self.min_steps else True if met_min_epochs and met_min_steps: should_stop = True else: log.info( "Trainer was signaled to stop but required minimum epochs" f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has" " not been met. Training will continue...") self.trainer.should_stop = should_stop return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0 @property def skip(self) -> bool: """Whether we should skip the training and immediately return from the call to :meth:`run`.""" # since `trainer.num_training_batches` depends on the `train_dataloader` but that won't be called # until `on_run_start`, we use `limit_train_batches` instead return self.done or self.trainer.limit_train_batches == 0 def connect( self, epoch_loop: TrainingEpochLoop) -> None: # type: ignore[override] """Connects a training epoch loop to this fit loop.""" self.epoch_loop = epoch_loop def reset(self) -> None: """Resets the internal state of this loop.""" if self.restarting: self.epoch_progress.reset_on_restart() def on_run_start(self) -> None: # type: ignore[override] """Calls the ``on_train_start`` hook.""" # reset train dataloader and val dataloader self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module) data_fetcher_cls = _select_data_fetcher(self.trainer) self._data_fetcher = data_fetcher_cls( prefetch_batches=self.prefetch_batches) self._is_fresh_start_epoch = True self._results.to(device=self.trainer.lightning_module.device) self.trainer._call_callback_hooks("on_train_start") self.trainer._call_lightning_module_hook("on_train_start") self.trainer._call_strategy_hook("on_train_start") def on_advance_start(self) -> None: # type: ignore[override] """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and ``on_train_epoch_start``""" model = self.trainer.lightning_module # reset train dataloader if not self._is_fresh_start_epoch and self.trainer._data_connector._should_reload_train_dl: log.detail( f"{self.__class__.__name__}: resetting train dataloader") self.trainer.reset_train_dataloader(model) self._is_fresh_start_epoch = False # reset outputs here instead of in `reset` as they are not accumulated between epochs self._outputs = [] if self.trainer.train_dataloader is not None and callable( getattr(self.trainer.train_dataloader.sampler, "set_epoch", None)): # set seed for distributed sampler (enables shuffling for each epoch) self.trainer.train_dataloader.sampler.set_epoch( self.epoch_progress.current.processed) # changing gradient according accumulation_scheduler self.trainer.accumulation_scheduler.on_train_epoch_start( self.trainer, self.trainer.lightning_module) # stores accumulated grad fractions per batch self.epoch_loop.batch_loop.accumulated_loss.reset( window_length=self.trainer.accumulate_grad_batches) self.epoch_progress.increment_ready() self.trainer._logger_connector.on_epoch_start() self.trainer._call_callback_hooks("on_epoch_start") self.trainer._call_lightning_module_hook("on_epoch_start") self.trainer._call_callback_hooks("on_train_epoch_start") self.trainer._call_lightning_module_hook("on_train_epoch_start") self.epoch_progress.increment_started() def advance(self) -> None: # type: ignore[override] """Runs one whole epoch.""" log.detail(f"{self.__class__.__name__}: advancing loop") assert self.trainer.train_dataloader is not None dataloader = self.trainer.train_dataloader assert self._data_fetcher is not None self._data_fetcher.setup(dataloader, batch_to_device=partial( self.trainer._call_strategy_hook, "batch_to_device", dataloader_idx=0)) with self.trainer.profiler.profile("run_training_epoch"): self._outputs = self.epoch_loop.run(self._data_fetcher) def on_advance_end(self) -> None: # inform logger the batch loop has finished self.trainer._logger_connector.epoch_end_reached() # get the model and call model.training_epoch_end model = self.trainer.lightning_module if is_overridden("training_epoch_end", model) and self._outputs: epoch_end_outputs = self.epoch_loop._prepare_outputs_training_epoch_end( self._outputs, lightning_module=model, num_optimizers=len(self.trainer.optimizers), ) # run lightning module hook training_epoch_end # refresh the result for custom logging at the epoch level epoch_end_outputs = self.trainer._call_lightning_module_hook( "training_epoch_end", epoch_end_outputs) if epoch_end_outputs is not None: raise MisconfigurationException( "`training_epoch_end` expects a return of None. " "HINT: remove the return statement in `training_epoch_end`." ) # free memory self._outputs = [] self.epoch_progress.increment_processed() # call train epoch end hooks self.trainer._call_callback_hooks("on_train_epoch_end") self.trainer._call_lightning_module_hook("on_train_epoch_end") self.trainer._call_callback_hooks("on_epoch_end") self.trainer._call_lightning_module_hook("on_epoch_end") self.trainer._logger_connector.on_epoch_end() if self.epoch_loop._num_ready_batches_reached(): self.epoch_loop.update_lr_schedulers( "epoch", update_plateau_schedulers=True) self.epoch_progress.increment_completed() # we manually decrease here because loggers expect that the same step is used when logging epoch-end metrics # even when the batch loop has finished self.epoch_loop._batches_that_stepped -= 1 # log epoch metrics self.trainer._logger_connector.update_train_epoch_metrics() self.epoch_loop._batches_that_stepped += 1 # if fault tolerant is enabled and process has been notified, exit. self.trainer._exit_gracefully_on_signal() def on_run_end(self) -> None: """Calls the ``on_train_end`` hook.""" log.detail(f"{self.__class__.__name__}: train run ended") # hook self.trainer._call_callback_hooks("on_train_end") self.trainer._call_lightning_module_hook("on_train_end") self.trainer._call_strategy_hook("on_train_end") def teardown(self) -> None: if self._data_fetcher is not None: self._data_fetcher.teardown() self._data_fetcher = None self.epoch_loop.teardown() def _should_accumulate(self) -> bool: """Whether the gradients should be accumulated.""" return self.epoch_loop._should_accumulate()
class FitLoop(Loop): """This Loop iterates over the epochs to run the training. Args: min_epochs: The minimum number of epochs max_epochs: The maximum number of epochs, can be set -1 to turn this limit off """ def __init__( self, min_epochs: Optional[int] = 1, max_epochs: int = 1000, ) -> None: super().__init__() if max_epochs < -1: # Allow max_epochs to be zero, since this will be handled by fit_loop.done raise MisconfigurationException( f"`max_epochs` must be a non-negative integer or -1. You passed in {max_epochs}." ) self.max_epochs = max_epochs self.min_epochs = min_epochs self.epoch_loop = TrainingEpochLoop() self.epoch_progress = Progress() self._is_fresh_start_epoch: bool = True @property def current_epoch(self) -> int: """Return the current epoch.""" return self.epoch_progress.current.completed @current_epoch.setter def current_epoch(self, value: int) -> None: """Setter for the current epoch.""" self.epoch_progress.current.completed = value @property def global_step(self) -> int: """Returns the global step.""" return self.epoch_loop.global_step @global_step.setter def global_step(self, value: int) -> None: """Sets the global step (forwards to epoch_loop)""" self.epoch_loop.global_step = value @property def total_batch_idx(self) -> int: """Returns the current batch index (across epochs)""" return self.epoch_loop.total_batch_idx @property def batch_idx(self) -> int: """Returns the current batch index (within this epoch)""" return self.epoch_loop.batch_idx @property def split_idx(self) -> Optional[int]: """Returns the index of the current batch split (within the current batch) for bptt.""" return self.epoch_loop.batch_loop.split_idx @property def min_steps(self) -> Optional[int]: # TODO(@justusschock): Why aren't we using the attribute in this class? """Returns the minimum numnber of steps to run.""" return self.epoch_loop.min_steps @min_steps.setter def min_steps(self, value: Optional[int]) -> None: """Sets the minimum number of steps (forwards to epoch_loop)""" # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided self.epoch_loop.min_steps = value @property def max_steps(self) -> int: """Returns the maximum number of steps to run.""" return self.epoch_loop.max_steps @max_steps.setter def max_steps(self, value: int) -> None: """Sets the maximum number of steps (forwards to epoch_loop)""" # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided if value is None: rank_zero_deprecation( "Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7." " Use `max_steps = -1` instead.") value = -1 elif value < -1: raise MisconfigurationException( f"`max_steps` must be a non-negative integer or -1 (infinite steps). You passed in {value}." ) self.epoch_loop.max_steps = value @property def running_loss(self) -> TensorRunningAccum: """Returns the running loss.""" return self.epoch_loop.batch_loop.running_loss @property def _skip_backward(self) -> bool: """Determines whether the loop will skip backward during automatic optimization.""" return self.epoch_loop.batch_loop.optimizer_loop._skip_backward @_skip_backward.setter def _skip_backward(self, value: bool) -> None: """Determines whether the loop will skip backward during automatic optimization.""" self.epoch_loop.batch_loop.optimizer_loop._skip_backward = value @property def _results(self) -> _ResultCollection: if self.trainer.training: return self.epoch_loop._results if self.trainer.validating: return self.epoch_loop.val_loop._results raise RuntimeError( "`FitLoop._results` property isn't defined. Accessed outside of scope" ) @property def done(self) -> bool: """Evaluates when to leave the loop. Returns True if trainer.should_stop was set (e.g. by early stopping) or if the maximum number of steps or epochs is reached. """ # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop stop_steps = _is_max_limit_reached(self.global_step, self.max_steps) stop_epochs = _is_max_limit_reached(self.current_epoch, self.max_epochs) should_stop = False if self.trainer.should_stop: # early stopping met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True met_min_steps = self.global_step >= self.min_steps if self.min_steps else True if met_min_epochs and met_min_steps: should_stop = True else: log.info( "Trainer was signaled to stop but required minimum epochs" f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has" " not been met. Training will continue...") self.trainer.should_stop = should_stop return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0 @property def skip(self) -> bool: """Whether we should skip the training and immediately return from the call to :meth:`run`.""" # since `trainer.num_training_batches` depends on the `train_dataloader` but that won't be called # until `on_run_start`, we use `limit_train_batches` instead return self.done or self.trainer.limit_train_batches == 0 def connect( self, epoch_loop: TrainingEpochLoop) -> None: # type: ignore[override] """Connects a training epoch loop to this fit loop.""" self.epoch_loop = epoch_loop def reset(self) -> None: """Resets the internal state of this loop.""" if self.restarting: self.epoch_progress.reset_on_restart() def on_run_start(self) -> None: # type: ignore[override] """Calls the ``on_train_start`` hook.""" # reset train dataloader and val dataloader self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module) self._is_fresh_start_epoch = True self._results.to(device=self.trainer.lightning_module.device) self.trainer._call_callback_hooks("on_train_start") self.trainer._call_lightning_module_hook("on_train_start") self.trainer._call_strategy_hook("on_train_start") def on_advance_start(self) -> None: # type: ignore[override] """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and ``on_train_epoch_start``""" model = self.trainer.lightning_module # reset train dataloader if not self._is_fresh_start_epoch and self.trainer._should_reload_dl_epoch: self.trainer.reset_train_dataloader(model) self._is_fresh_start_epoch = False if self.trainer.train_dataloader is not None and callable( getattr(self.trainer.train_dataloader.sampler, "set_epoch", None)): # set seed for distributed sampler (enables shuffling for each epoch) self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch) # changing gradient according accumulation_scheduler self.trainer.accumulation_scheduler.on_train_epoch_start( self.trainer, self.trainer.lightning_module) # stores accumulated grad fractions per batch self.epoch_loop.batch_loop.accumulated_loss.reset( window_length=self.trainer.accumulate_grad_batches) self.epoch_progress.increment_ready() def advance(self) -> None: # type: ignore[override] """Runs one whole epoch.""" dataloader = self.trainer.strategy.process_dataloader( self.trainer.train_dataloader) data_fetcher = self.trainer._data_connector.get_profiled_dataloader( dataloader) with self.trainer.profiler.profile("run_training_epoch"): self.epoch_loop.run(data_fetcher) # the global step is manually decreased here due to backwards compatibility with existing loggers # as they expect that the same step is used when logging epoch end metrics even when the batch loop has # finished. this means the attribute does not exactly track the number of optimizer steps applied. # TODO(@carmocca): deprecate and rename so users don't get confused self.global_step -= 1 # log epoch metrics self.trainer.logger_connector.update_train_epoch_metrics() self.global_step += 1 def on_advance_end(self) -> None: self.epoch_progress.increment_completed() def on_run_end(self) -> None: """Calls the ``on_train_end`` hook.""" # NOTE: the current_epoch is already incremented # Lightning today does not increment the current epoch at the last epoch run in Trainer.fit # To simulate that current behavior, we decrement here. # TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007 self.current_epoch = max(self.current_epoch - 1, 0) # hook self.trainer._call_callback_hooks("on_train_end") self.trainer._call_lightning_module_hook("on_train_end") self.trainer._call_strategy_hook("on_train_end") # give accelerators a chance to finish self.trainer.strategy.on_train_end() def teardown(self) -> None: self.epoch_loop.teardown() def _should_accumulate(self) -> bool: """Whether the gradients should be accumulated.""" return self.epoch_loop._should_accumulate()